A novel Parallel Cooperative Mean-Teacher framework (PCMT) combined with prediction uncertainty guide and class contrastive learning for semi-supervised polyp segmentation

Abstract

Polyp segmentation technology based on deep learning can quickly and accurately help doctors locate lesions, but its development is limited by pixel-level annotations. The polyp segmentation methods based on semi-supervised learning(SSL) is an effective solution to alleviate annotation pressure. For mainstream semi-supervised polyp segmentation methods, due to the extremely deep depth of the polyp segmentation network and the limited use of labeled data for training, the polyp segmentation network is prone to feature expression bottlenecks. And it is a deviation direction to refine pseudo-labels using regional mathematical evaluation, rather than using pixel-level pseudo label refinement schemes. To solve the limitation, based on SSL, a novel Parallel Cooperative Mean-Teacher framework (PCMT) is proposed. PCMT has several effective components: (1) using the segmentation network with deep supervision branches,which is trained by the Parallel Cooperative Mean-Teacher framework for solving feature expression bottlenecks, (2) prediction uncertainty guide for providing more reliable pseudo-labels, (3) class contrastive regularization for strengthening the affinity between the foreground and background features. PCMT and state-of-the-art semi-supervised methods have been compared using 10%, 20%, 50%, and 100% labeled data on five public datasets, respectively. A large number of experimental results show that PCMT has more prominent prediction performance and better generalization ability. Meanwhile, the segmentation performance of PCMT with only requiring 20% labeled data could reach that of full supervised methods with requiring 100% labeled data. Above the results prove that it is feasible of PCMT for semi-supervised polyp segmentation tasks.